VTaMo: Video-Text Alignment Model for Sign Language Translation

2026-07-10 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

VTaMo: Video-Text Alignment Model for Sign Language Translation converts continuous sign videos into spoken language text. Existing gloss-free approaches use pre-trained visual encoders and language models but rely on implicit cross-modal alignment. VTaMo addresses this by introducing a framework with explicit multi-granularity alignment at three levels. First, local alignment uses entropy-regularized optimal transport with a learnable null token for fine-grained frame-to-token correspondences. Second, global alignment employs a learnable orthogonal transformation to calibrate embedding space geometry via Earth Mover's Distance. Third, position-aligned contrastive learning generates discriminative token-level representations. Experiments on Phoenix-2014T, CSL-Daily, How2Sign, and OpenASL datasets show consistent state-of-the-art performance, with ablations confirming each component's contribution. Code is available at https://github.com/junyi2005/vtamo.

Key takeaway

For NLP Engineers developing sign language translation systems, VTaMo's explicit multi-granularity alignment offers a robust approach to overcome implicit cross-modal alignment limitations. You should consider integrating its local, global, and position-aligned contrastive learning components to achieve state-of-the-art performance. This framework provides concrete methods to enhance frame-to-token correspondences and discriminative representations, directly improving translation accuracy.

Key insights

VTaMo explicitly aligns video frames and text tokens at multiple granularities for improved sign language translation.

Principles

• Explicit multi-granularity alignment enhances SLT.
• Optimal transport refines frame-to-token links.
• Orthogonal transformations calibrate embedding spaces.

Method

VTaMo integrates local alignment via entropy-regularized optimal transport, global alignment using a learnable orthogonal transformation with Earth Mover's Distance, and position-aligned contrastive learning.

In practice

• Apply entropy-regularized optimal transport for fine-grained alignment.
• Use orthogonal transformations to calibrate embedding spaces.
• Implement position-aligned contrastive learning for token representations.

Topics

• Sign Language Translation
• Video-Text Alignment
• Multi-Granularity Alignment
• Optimal Transport
• Contrastive Learning
• Cross-Modal Alignment

Code references

• junyi2005/vtamo

Best for: Research Scientist, AI Scientist, NLP Engineer, Computer Vision Engineer

Related on AIssential

• VTaMo: Video-Text Alignment Model for Sign Language Translation — Explicit multi-granularity alignment significantly improves gloss-free sign language translation by resolving vision-text semantic and temporal mismatches.
• Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field — Consistent evaluation reveals many reported SLT performance gains are artifacts of inconsistent implementation and evaluation setups.
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• Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models — LLM-generated target-side paraphrases can augment Sign Language Translation, improving semantic fidelity, but effectiveness varies by corpus characteristics.
• M3T: Discrete Multi-Modal Motion Tokens for Sign Language Production — Integrating high-dimensional facial models and collapse-free tokenization is crucial for linguistically complete sign language production.
• Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning — TCA-Captioner enhances audiovisual video captioning by achieving precise temporal and cross-modal alignment through an iterative refinement strategy.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.